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Most human math phd's have all kinds of shortcomings. The idea that finding some "gotchas" shows that they are miles off the mark with the hype is absurd.
by danielmarkbruce 2y ago
Most human math phd's have all kinds of shortcomings. The idea that finding some "gotchas" shows that they are miles off the mark with the hype is absurd.
- michaelt 2y ago> Most human math phd's have all kinds of shortcomings. I know a great many people with PhDs. They're certainly not infallible by any means, but I can assure you, every single one of them can correctly count the number of occurrences of the letter 'r' in 'strawberry' if they put their mind to it.
- danielmarkbruce 2y agoI'll bet said phds can't answer the equivalent question in a language they don't understand. LLMs don't speak character level english. LLMs are, in some stretched meaning of the word, illiterate. If LLMs used character level tokenization it would work just fine. But we don't do that and accept the trade off. It's only folks who have absolutely no idea how LLMs work that find the strawberry thing meaningful.
- sdesol 2y ago> It's only folks who have absolutely no idea how LLMs work that find the strawberry thing meaningful. I think it is meaningful in that it highlights how we need to approach things a bit differently. For example, instead of asking "How many r's in strawberry?", we say "How many r's in strawberry? Show each character in an ordered list before counting. When counting, list the position in the ordered list." If we do this, every model that I asked got it right. https://beta.gitsense.com/?chat=167c0a09-3821-40c3-8b0b-8422223ff656 https://beta.gitsense.com/?chat=167c0a09-3821-40c3-8b0b-8422... There are quirks we need to better understand and I would say the strawberry is one of them. Edit: I should add that getting LLMs to count things might not be the best way to go about it. Having it generate code to count things would probably make more sense.
- HarHarVeryFunny 2y agoI was impressed with Claude Sonnet the other day - gave it a photo of my credit card bill (3 photos actually - long bill) and asked it to break it down by recurring categories, counting anything non-recurring as "other". It realized without being asked that a program was needed, and wrote/ran it to give me what I asked for.
- sdesol 2y agoI think this will be the future. LLMs will know enough to know that it should hand things off to something else.
- danielmarkbruce 2y agoIt's the present. ChatGPT, for example, is an application. It uses models, but it does all kinds of stuff at the application level too.
- sdesol 2y agoIs the instructions to pass something off built into the model or is it clever prompting or a bit of both?
- HarHarVeryFunny 2y agoIn general it's "tool use" where the model's system prompt tells it to use certain tools for certain tasks, and having been trained to follow instructions, it does so!
- fzzzy 2y agoYes, you should say "could you please write and execute a program to count the number of "r" characters in the string "strawberry"
- danielmarkbruce 2y agoIt's not that hard of a problem to solve at the application level. It's just hard to get a single model to do all the things.
- wat10000 2y agoI’ll bet said PhDs will tell you they don’t know instead of confidently stating the wrong answer in this case. Getting LLMs to express an appropriate level of confidence in their output remains a major problem.
- michaelt 2y agoYou say that very confidently - but why shouldn't an LLM have learned a character-level understanding of tokens? LLMs would perform very badly on tasks like checking documents for spelling errors, processing OCRed documents, pluralising, changing tenses and handling typos in messages from users if they didn't have a character-level understanding. It's only folks who have absolutely no idea how LLMs work that would think this task presents any difficulty whatsoever for a PhD-level superintelligence :)
- fzzzy 2y agoThe llm has absolutely no way of knowing which characters are in which token.
- danielmarkbruce 2y agoLLMs are fed token ids, out of a tokenizer.... no characters. They don't even have any concept of a character. You are in a discussion where you are just miles out of your depth. Go read LLMs 101 somewhere.
- michaelt 2y agoIf the LLM hasn't learned the letters that comprise input tokens, how do you explain this sort of behaviour? https://chatgpt.com/share/678e95cf-5668-8011-b261-f96ce5a33a5a https://chatgpt.com/share/678e95cf-5668-8011-b261-f96ce5a33a... It can literally spell out words, one letter per line. Seems pretty clear to me the training data contained sufficient information for the LLM to figure out which tokens correspond to which letters. And it's no surprise the training data would contain such content - it'd be pretty easy to synthetically generate misspellings, and being able to deal with typos and OCR mistakes gracefully would be useful in many applications.
- danielmarkbruce 2y agoTwo answers: 1 - ChatGPT isn't an LLM, its an application using one/many LLMs and other tools (likely routing that to a split function). 2 - even for a single model 'call': It can be explained with the following training samples: "tree is spelled t r e e" and "tree has 2 e's in it" The problem is, the LLM has seen something like: 8062, 382, 136824, 260, 428, 319, 319 and 19816, 853, 220, 17, 319, 885, 306, 480 For a lot of words, it will have seen data that results in it saying something sensible. But it's fragile. If LLMs used character level tokenization, you'd see the first example repeat the token for e in tree rather than tree having it's own token. There are all manner of tradeoffs made in a tokenization scheme. One example is that openai made a change in space tokenization so that it would produce better python code.
- HarHarVeryFunny 2y agoI don't think that (sub-word) tokenization is the main difficulty. Not sure which models still fail the "strawberry" test, but I'd bet they can at least spell strawberry if you ask, indicating that breaking the word into letters is not the problem. The real issue is that you're asking a prediction engine (with no working memory or internal iteration) to solve an algorithmic task. Of course you can prompt it to "think step by step" to get around these limitations, and if necessary suggest an approach (or ask it to think of one?) to help it keep track of it's letter by letter progress through the task.
- danielmarkbruce 2y agoBreaking words/tokens is very explicitly the problem.
- HarHarVeryFunny 2y agoNo ... try claude.ai or meta.ai (both behave the same) by asking them how many r's in the (made up) word ferrybridge. They'll both get it wrong and say 2. Now ask them to spell ferrybridge. They both get it right. gemini.google.com still fails on "strawberry" (the other two seem to have trained on that, which is why i used a made up word instead), but can correctly break it into a letter sequence if asked.
- danielmarkbruce 2y agoYep, if by chance you hit a model that has seen the training data that happens to shove those tokens together in a way that it can guess, lucky you. The point is, it would be trivial for an LLM to get it right all the time with character level tokenization. The reason LLMs using the current tokenization best tradeoff find this activity difficult is that the tokens that make up tree don't include the token for e.
- HarHarVeryFunny 2y agoNo - you can give the LLM a list of letters and it STILL won't be able to count them reliably, so you are guessing wrong about where the difficult lies. Try asking Claude: how many 'r's are in this list (just give me a number as your response, nothing else) : s t r a w b e r r y
- throwaway2037 2y ago> LLMs are, in some stretched meaning of the word, illiterate. You raise an interesting point here. How would LLMs need to change for you to call them literate? As a thought experiment, I can take a photograph of a newspaper article, then ask a LLM to summarise it for me. (Here, I assume that LLMs can do OCR.) Does that count?
- danielmarkbruce 2y agoIt's a bit of a stretch to call them illiterate, but if you squint, it's right. The change is easy - get rid of tokenization and feed in characters or bytes. The problem is, that causes all kinds of other problems with respect to required model size, required training, and so on. It's a researchy thing, I doubt we end up there any time soon.
- CamperBob2 2y agoI know a great many people with PhDs. They're certainly not infallible by any means, but I can assure you, every single one of them can correctly count the number of occurrences of the letter 'r' in 'strawberry' if they put their mind to it. So can the current models. It's frustrating that so many people think this line of reasoning actually pays off in the long run, when talking about what AI models can and can't do. Got any other points that were right last month but wrong this month?
- danielmarkbruce 2y agoThere are always going to be doubters on this. It's like the self driving doubters. Until you get absolute perfection, they'll point out shortcomings. Never mind that humans have more holes than swiss cheese.
- visarga 2y agoHumans tasked to count how many vowels are in "Pneumonoultramicroscopicsilicovolcanoconiosis" (a real word), without seeing the word visually, just from language, would struggle. Working memory limits. We're not that different, we fail too.